How can 3 same size CNN layers in different ordering output different receptive field from the input layer?
It is really easy to visualize the growth in the receptive field of the input as you go deep into the CNN layers if you consider a small example. 

Lets take a simple example :

Dimensions are in the form of (channels x height x width)

Input Image - 3x5x5 (I)

Conv 1 kernel - 3x2x2 (K1) : we consider only 1 filter

Conv 2 kernel - 1x2x2 (K2)

Pad = 0 (P)

Stride = 1 (S)

The output dimensions are calculated by the following formula taken from the lecture CS231n.

Output = (I - K + 2P)/S + 1

When you do a convolution of the input image with the first filter K1, you get an output of 1x4x4 (this is the activation of CONV1 layer). The receptive field for this is same as the kernel size (K1) that is 2x2.
When this layer (1x4x4) is convolved with the second kernel (CONV2) 1x2x2 (K2), the output would be 1x3x3. The receptive field for this would be 3x3 window of the input because you have already accumulated the sum of the 2x2 window in the previous layer. 

Considering your example of three CONV layers with 3x3 kernels is also similar. The first layer activation accumulates the sum of all the neurons in the 3x3 window of the input. When you further convolve this output with a kernel of 3x3, it will accumulate all the outputs of the previous layers covering a bigger receptive field of the input. 

This observation comes in line with the argument that deeper layers learn more intricate features like facial expressions, abstract concepts etc because they cover a larger receptive field of our original input image.

Hope this clarifies.